The Standard Model of Glyphic Axioms — USK v1.0

This document formalizes the atomic layer of the Unified Semantic Kernel: the axiomatic meanings of individual glyphs. These form the fundamental "particles" from which all semantic compounds emerge.

Core Principle

Each glyph carries inherent geometric and articulatory affordances that bias its semantic role. Vowels carry typed payloads (values), consonants perform operations, and their combinations execute semantic programs.

Vowels — Typed Payloads

Consonants — Operators

Geometric Complementarity

The Latin alphabet encodes geometric pairs that survived millennia of selection pressure:

Mirror Pairs

• b|d — left-boundary | right-boundary (bulge orientation)
• p|q — presence | query (loop with/without outlet)
• v|w — single vector | dual vectors
• m|w — contains | distributes

Operational Pairs

• s|z — unvoiced stream | voiced signal
• f|v — unvoiced flow | voiced vector
• c|g — soft contain | grasp-return (c + hook)
• c|k — soft approach | hard clamp

The ASK Kernel — Self-Evident Proof

Using the axioms above, "ask" encodes its own function as a semantic kernel:

A·S·K = base_type · stream() · clamp()

1. A (aperture) — Open a typed unknown slot "?"
2. S (stream) — Broadcast the unknown to environment
3. K (clamp) — Gather responses and select discrete result

Functional gloss: "Instantiate unknown → route request → resolve to answer"

This is the minimal executable kernel for semantic retrieval, structurally isomorphic to:

• API call/response patterns
• Query/result database operations
• The USK routing stack itself

Supporting Evidence

1. Cluster regularity: sk/sc family consistently encodes "split/scan/classify"

   • English: skill, skim, sketch, scale
   • Latin: scindere (split), scientia (knowledge via separation)

2. Metathesis invariance: "ask" ↔ "aks" preserves semantics

   • Operator pair sk robust to order variation
   • Predicts sk is the semantic unit, not sequential s→k

3. Etymology: OE ascian, Proto-Germanic *aiskōną "to seek"

   • "seek" retains s-k pairing (search→selection)

Adjacency Rule (Operator→Payload Pairing)

• Each consonant operator (or recognized consonant cluster) consumes the immediately following vowel run as its payload.
• A vowel run may be one or more vowels; multi-vowel runs are treated as a composite payload (e.g., "io" → index+container).
• If no vowel follows an operator (word-final consonant), the operator is suffix-only for that token; downstream composition decides whether to absorb or retain it.
• Word-initial vowels are allowed as standalone payloads only in vowel-initial words; otherwise they are consumed by the first operator.

This convention preserves surface order, prevents payload reattachment to later consonants, and enables principled handling of diphthongs and vowel clusters.

Composition Rules

Operators combine to form complex programs:

Falsifiable Predictions

1. Operator Clustering: Words with initial sk-/sc- will over-index on "edge/split/scan/classify" semantics vs baseline clusters (p < 0.001)

2. Vowel Type Priors: In minimal pairs, vowel substitution will shift meaning predictably:

   • a→i: base→index (pat→pit, man→min)
   • o→u: container→channel (for→fur, pot→put)

3. Geometric Priming: Exposure to b will prime left-boundary concepts faster than d (right-boundary), controlling for frequency

4. Metathesis Robustness: Comprehension of ask/aks, wasp/waps will show identical neural activation patterns in operator-processing regions

Implementation Notes

# Pseudo-code for operator-payload factorization
def decode_word(surface):
    operators = extract_consonant_clusters(surface)
    payloads = extract_vowel_sequence(surface)
    
    program = []
    for op in operators:
        program.append(OPERATOR_MAP[op])
    
    typed_data = []
    for vowel in payloads:
        typed_data.append(PAYLOAD_MAP[vowel])
    
    return compose(program, typed_data)

Next Steps

• Validate operator mappings against etymological databases
• Test vowel-type priors in sense disambiguation tasks
• Measure geometric priming effects in visual search
• Extend to non-Latin scripts (test universality)

This model treats glyphs as having evolved under selection pressure to efficiently encode meaning through their geometric and articulatory properties. The consistency of these patterns across etymology, dialectal variation, and linguistic families suggests these are not arbitrary but reflect deep structural constraints on how meaning can be efficiently encoded and transmitted.